Processing methodology for full-waveform sonic wavefield separation

ABSTRACT

A method for full-waveform sonic (FWS) wavefield separation includes receiving FWS data; performing an anti-aliasing linear Radon transform on the received FWS data; extracting Radon-transformed FWS data corresponding to a wave component using a slanted window; and determining signals of the wave component by performing an inverse Radon transform on the extracted Radon-transformed FWS data.

TECHNICAL FIELD

This disclosure relates to full-waveform sonic (FWS) data processing.

BACKGROUND

Full-waveform sonic (FWS) devices can be used in wellbores to collectFWS data which can be processed to provide information about lithologicand fluid properties of a formation. Different from other geophysicalmeasurements such as seismic and vertical seismic profile (VSP), FWSdata is typically measured using a small sampling time interval, but alarge offset spacing, thus contributing to an aliasing effect in the FWSdata. Existing FWS processing methods overlook the aliasing effect andsimply apply processing technologies that are developed in other fieldsto FWS data, causing uncertainty and inaccuracy in FWS wavefieldseparation results.

SUMMARY

The present disclosure describes methods and systems, includingcomputer-implemented methods, computer program products, and computersystems of a processing methodology for full-waveform sonic (FWS)wavefield separation.

In some implementations, FWS data is received. An anti-aliasing linearRadon transform is performed on the received FWS data. Radon-transformedFWS data corresponding to a wave component is extracted using a slantedwindow. Signals of the wave component are determined by performing aninverse Radon transform on the extracted Radon-transformed FWS data.

The previously-described implementation is implementable using acomputer-implemented method; a non-transitory, computer-readable mediumstoring computer-readable instructions to perform thecomputer-implemented method; and a computer-implemented systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method/theinstructions stored on the non-transitory, computer-readable medium.

The subject matter described in this disclosure can, in someimplementations, efficiently separate FWS wave components free ofaliasing artifacts by using anti-aliasing linear Radon transform (AALRT)and adaptive slant extraction (ASE). AALRT uses an anti-aliasing offsetspacing determined by a designated velocity value to enable efficientlocalized interpolation of FWS data. ASE can adaptively adjust a slantwindow based on the FWS offset range so that a target wave can beextracted without including some or all undesired wave components.Comparing with a manual extraction method, ASE can save time and reducehuman bias. Other advantages will be apparent to those of ordinary skillin the art.

The details of one or more implementations of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription. Other features, aspects, and advantages of the subjectmatter will become apparent from the description, the drawings, and theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flowchart of an example method for full-waveform sonic (FWS)wavefield separation, according to some implementations.

FIGS. 2A-2C illustrate FWS wavefield separation for a compressionalwave, according to some implementations.

FIGS. 3A-4C illustrate FWS wavefield separation for a synthetic FWSgather, according to some implementations.

FIGS. 5A-5D illustrate FWS wavefield separation for a real FWS gather,according to some implementations.

FIG. 6 is a block diagram illustrating an example computer system usedto provide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the instant disclosure, according to some implementations.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

The following detailed description describes a processing methodologyfor full-waveform sonic (FWS) wavefield separation and is presented toenable any person skilled in the art to make and use the disclosedsubject matter in the context of one or more particular implementations.Various modifications, alterations, and permutations of the disclosedimplementations can be made and will be readily apparent to thoseskilled in the art, and the general principles defined may be applied toother implementations and applications without departing from scope ofthe disclosure. Thus, the present disclosure is not intended to belimited to the described or illustrated implementations, but is to beaccorded the widest scope consistent with the principles and featuresdisclosed.

FWS devices can be used in wellbores to collect FWS data that can beprocessed to provide information about seismic and lithologic propertiesof a formation. FWS data can include information of linear orquasi-linear refraction waves, for example, compressional, shear, andStoneley waves, characterized by different wave velocities. Linear Radontransform (or slant stack) can be used to extract different wavecomponents for FWS wavefield separation. Although Radon transform hasbeen used for seismic and VSP processing, usage on FWS data processinghas been limited. Compared with seismic recording, FWS samples inspatial direction (that is, offset direction) are rather sparse thanthose in time direction. Due to the sparse spatial samples, an aliasingeffect exists that hinders direct applications of Radon transformmethodologies developed for seismic processing to FWS data.

Linear Radon transform transforms data from an offset(x)−time(t) domaininto a slowness(p)−intercept(τ) Radon parameter domain based on arelationship of t=px+τ, where offset x can represent a distance betweena source transmitter and a receiver, τ indicates a travel time at zerooffset (i.e. x=0), and slowness p can relate to a velocity v by p=1/v.For FWS data, due to dense time samples and sparse spatial samples,aliasing usually occurs in the slowness(p) direction. Based on NyquistLaw, an anti-aliasing condition to regulate the slowness sampling in theRadon parameter domain can be expressed as

$\begin{matrix}{{{\Delta \; p} < \frac{1}{f_{\max}*\left( {{x}_{\max} - {x}_{\min}} \right)}},{{{or}\mspace{14mu} p} < \frac{1}{f_{\max}*\Delta \; x}},} & (1)\end{matrix}$

where f_(max) is the maximum effective frequency of FWS data, Δx is anoffset spacing, |x|_(max) and |X|_(min) are the maximum and minimumoffset distance in absolute value. For some FWS devices, the samplingtime interval (that is, sampling time interval when recording FWS data)and offset spacing in FWS measurements are 2e-5 second (s) and 0.1524meter (m), respectively. The Nyquist Law indicates that the maximumeffective frequency can be

$f_{\max} = {\frac{1}{2 \times 2e^{- 5}} = {25,000\mspace{14mu} {{Hz}.}}}$

Based on Equation (1), the slowness

${p < \frac{1}{25,000 \times 0.1524}} = {0.0003\mspace{14mu} s\text{/}m}$

and the velocity

${v > \frac{1}{0.0003}} = {3810\mspace{14mu} m\text{/}{s.}}$

In other words, the velocity range free of aliasing artifacts for theabove FWS measurement setting is larger than 3810 m/s, exceeding shearand Stoneley wave velocities of most formations. In some other cases,FWS data can be acquired by FWS devices which can use an even smallersampling time interval and a larger offset spacing, for example, 1e-5 sand 0.3048 m, and the wave velocity should be larger than 15240 m/s toavoid aliasing. In other words, many FWS wave components can beindistinguishable under aliasing artifacts.

At a high level, the described approach describes a processing flow forFWS wavefield separation by taking advantage of FWS wavefield features.The described approach uses anti-aliasing linear Radon transform (AALRT)and adaptive slant extraction (ASE) to separate wavefields. AALRTinterpolates FWS data based on an anti-aliasing offset spacing. AALRTcan flexibly tackle FWS aliasing effects based on an actual formationvelocity range and thus generate a high-quality Radon image forwavefield separation. ASE can extract each wave component using a slantwindow that can adaptively fit the actual FWS configuration withoutincluding other wave component information.

In some implementations, FWS data is received. The received FWS data canbe a common shot gather. An anti-aliasing linear Radon transform can beperformed on the received FWS data. Radon-transformed FWS datacorresponding to a wave component can be extracted using a slantedwindow. Signals of the wave component can be determined by performing aninverse Radon transform on the extracted Radon-transformed FWS data. Thewave component can be one of a compressional wave, a shear wave, or aStoneley wave. In some implementations, performing the anti-aliasinglinear Radon transform can include determining a set of anti-aliasingoffset values and a set of slowness values based on an anti-aliasingoffset spacing, generating interpolated FWS data based on the receivedFWS data and the set of anti-aliasing offset values, and performing aRadon transform on the interpolated FWS data based on the set ofanti-aliasing offset values and the set of slowness values. Theanti-aliasing offset spacing can be Δx_(a)=0.5*Δt*V_(min), where Δt is asampling time interval of the received FWS data, and V_(min) is a lowerboundary of a velocity of the wave component. In some implementations, avelocity analysis can be performed to determine a central velocity, avelocity range, a central time, and a time range of the wave component.The slanted window can be centered at a point determined based on thecentral velocity and the central time of the wave component, and theslanted window can have a size determined based on the velocity rangeand the time range of the wave component. In some implementations,performing an inverse Radon transform on the extracted Radon-transformedFWS data can include performing the inverse Radon transform based on aset of actual offset values.

FIG. 1 is a flowchart of an example method 100 for FWS wavefieldseparation, according to some implementations. For clarity ofpresentation, the description that follows generally describes method100 in the context of the other figures in this disclosure. For example,method 100 can be performed by a computer system described in FIG. 6, orany suitable system, environment, software, and hardware, or acombination of systems, environments, software, and hardware asappropriate. In some implementations, various steps of method 100 can berun in parallel, in combination, in loops, or in any order.

At a high level, the method 100 includes the following main steps: (1)information check and pre-processing; (2) anti-aliasing Linear RadonTransform; (3) adaptive slant extraction; and (4) inverse radontransform. At block 102, the method 100 includes receiving andpre-processing FWS data (or traces) of a target region. For example, thetarget region can include one or more earth subsurface layers. A FWSdevice including source(s) and receivers can be lowered down into aborehole in the target region. The FWS source can send acoustic waves orshots into the borehole and adjacent rock formations, and the receiverscan measure and record refracted or reflected waves. The receiver cansample the received waveform in time with a sampling interval and recordthe sampled data. In some cases, the source can generate acoustic wavesat different locations for the receivers to record reflected orrefracted waves. For example, the FWS device can be lowered to a firstdepth in the borehole and the FWS source can transmit a first waveformor shot for the receivers to record refracted or reflected waves. TheFWS device can then be moved to a second depth in the borehole andtransmit a second waveform or shot. The recorded data at one receivercorresponding to a single source wave transmission or shot can be calleda trace. In some implementations, the received FWS data at block 102 isa common shot gather including traces from a single shot recorded atmany receivers.

The received FWS data can be pre-processed to generate information thatcan be used in the processing steps of blocks 104 and 106. Thepre-processing can include interactive velocity analysis, linear moveoutcorrection, and other pre-processing steps. The pre-processing can alsodetermine a time range, an offset range, and a frequency range of thereceived FWS data. For example, the frequency range can be determined byperforming a Fourier transform of the received FWS traces andidentifying the maximum signal frequency in Fourier domain. Theinteractive velocity analysis can determine time and velocityinformation for each wave component. The velocity analysis can generatea velocity file including multiple types of information for each wavecomponent, for example, a central time and a central velocity, as wellas an upper boundary and a lower boundary for both time and velocity.The central velocity and time can be used not only in linear moveoutcorrection to flatten dipped wave events, but also in determining acentral location (τ_(c), p_(c)) of the wave component in Radon domain.The upper and lower boundary for velocity and time (that is, thevelocity range and the time range), as well as (τ_(c), p_(c)), can beused for determining a slant window of ASE in block 106.

At block 104, an AALRT is performed on the received FWS data. In AALRT,instead of conventionally employed trace interpolation, a localizedinterpolation scheme is used in a Radon parameter domain to densify datainformation available. Based on the anti-aliasing condition in Equation(1), an anti-aliasing offset spacing can be defined asΔx_(a)=0.5*Δt*V_(min), where Δt is the sampling time interval whensampling received waves to record FWS data, and V_(min) is a lowerboundary of the wave velocity. For example, in block 102, a velocitylower boundary can be determined for each of the compressional, shear,and Stoneley waves, and V_(min) can be the smallest of the three lowerboundary values. Note that Δx_(a) is a theoretical offset spacing forwave components with a velocity larger than V_(min) to be effectivelymeasured, processed, and free from aliasing artifacts. An anti-aliasingoffset spread x_(a) can be generated ranging from the first measuredoffset to the last measured offset and spaced by Δx_(a). For example, ifan actual offset spread (that is, actual distances between the sourceand multiple receivers in the FWS data acquisition) can be expressed asa vector x=[0.15 0.3 0.45 0.6] m with an offset spacing 0.15 m. Using ananti-aliasing offset spacing Δx_(a)=0.05 m, the anti-aliasing offsetspread can be expressed as a vector x_(a)=[0.15 0.2 0.25 0.3 0.35 0.40.45 0.5 0.55 0.6] m.

For interpolating the received FWS data, the FWS common shot gather canbe formed and transformed into a frequency domain (known as F-X domain).The FWS gather in the frequency domain can be used to determine linearrepresentations such as gradient g and intercept i at the actual offsetspread x based on

$\begin{matrix}{{{\left\lbrack {{{Diag}(x)}\mspace{14mu} {{Diag}(1)}} \right\rbrack \begin{bmatrix}g \\i\end{bmatrix}} = {d(f)}},} & (2)\end{matrix}$

where Diag(x) is a diagonal matrix whose diagonal elements are theactual offset spread x, Diag(1) has the same size as Diag(x) and is adiagonal matrix whose diagonal elements are one, g and i are each acolumn vector having a same number of elements as the vector x, and d(f)is filtered FWS data centered around a frequency f. For example, d(f)can be generated by performing a discrete Fourier transform on thecommon shot gather, trace by trace, along the time direction, and d(f)can include Fourier transformed data at the particular frequency f fromall or a subset of all traces. Equation (2) can be an under-determinedinversion, and many regularization methods can be used to generatesmooth estimates of g and i. In some implementations, Equation (2) canbe solved for each frequency point from the discrete Fourier transform.

After g and i are determined, interpolated data d_(a)(f) onanti-aliasing offset spread x_(a) can be generated by performing a localinterpolation. In some implementations, the local interpolation can be aforward application of Equation (2) by replacing x with x_(a) andinterpolating g and i for the anti-aliasing offset spread x_(a). Forexample, the interpolated data d_(a)(f) can be determined by

${{d_{a}(f)} = {\left\lbrack {{{Diag}\left( x_{a} \right)}\mspace{14mu} {{Diag}(1)}} \right\rbrack \begin{bmatrix}g_{a} \\i_{a}\end{bmatrix}}},$

where Diag(x_(a)) is a diagonal matrix whose diagonal elements are theanti-aliasing offset spread x_(a), Diag(1) has the same size asDiag(x_(a)) and is a diagonal matrix whose diagonal elements are one,and g_(a) and i_(a) each is a column vector having a same number ofelements as the vector x_(a).

The vectors g_(a) and i_(a) can be interpolated based on g and i using aprinciple of proximity. In other words, the gradient and intercept at ananti-aliasing offset is determined by the gradient and intercept at anactual offset that is closest to the anti-aliasing offset. For example,for an actual offset spread x=[0.15 0.3 0.45] m, g and i can beexpressed as g=[g₁ g₂ g₃]^(T) and i=[i₁, i₂, i₃]^(T), respectively,where T denotes transpose. If the anti-aliasing offset spread isx_(a)=[0.15 0.2 0.25 0.3 0.35 0.4 0.45] m, since the anti-aliasingoffset 0.2 m is closest to the actual offset 0.15 m, the gradient andintercept g₁ and i₁ at the actual offset 0.15 m can be used as thegradient and intercept at the anti-aliasing offset 0.2 m. In otherwords, g_(a)=[g₁, g₁, g₂, g₂, g₂, g₃, g₃] and i_(a)=[i₁, i₁, i₂, i₂, i₂,i₃, i₃].

The slowness sampling values p_(a) in the Radon parameter domaincorresponding to the anti-aliasing offset spread x_(a) can be determinedby substituting x_(a) into the anti-aliasing condition of Equation (1).In other words, the ith element in the slowness sampling vector p_(a)can be determined by

$p_{a{(i)}} = \frac{1}{f_{\max}*x_{a{(i)}}}$

where x_(a(i)) is the ith element of the anti-aliasing offset vectorx_(a), and f_(max) can be the maximum signal frequency of FWS datadetermined in block 102.

Radon transform of the interpolated FWS data on the anti-aliasing offsetspread x_(a) can be formulated as the following inversion form,

Lm=d _(a)(f), where L _(j,n)=exp(−i2πfp _(a(j)) x _(a(n))),  (3)

x_(a(n)) is the nth element of the anti-aliasing offset vector x_(a),p_(a(j)) is the jth element of the slowness sampling vector p_(a), andL_(j,n) is the (j,n)th element of matrix L. A regularized solution toEquation (3) can generate Radon transformed data for the frequency f as

m=L ^(H)(LL ^(H) +μI)⁻¹ d _(a)(f),  (4)

where L^(H) denotes a Hermitian transpose of L, μ is pre-whiteningfactor to suppress noise influence. In some implementations, μ can bedetermined as a percentage (for example, a percentage between 0.1%˜1%)of the maximum absolute value of LL^(H). In some implementations,Equation (4) is used to generate the Radon transformed data for eachfrequency, and a Radon image is generated by transforming the Radontransformed data of all or a subset of all frequencies back into thetime domain using an inverse Fourier transform. In other words, Equation(4) can be used to generate a Radon image of the interpolated FWS data.Even though Equation (3) has a similar form and solution as other Radontransform types, Equation (3) is different from others on two aspects:(1) x_(a) is calculated and employed as the anti-aliasing offset spreadthat can well model the desired velocity range; (2) to compensate datainformation over x_(a), the localized interpolation can obtain d_(a)efficiently, and localized details on frequency and offset are greatlyprotected in the process.

At block 106, a slanted window can be used to extract a wave componentfrom the Radon image generated in block 104. Cross-hatched energyclusters are typically observed in a Radon image due to finite spatialoffsets, therefore routinely-employed rectangular windows areinefficient to extract the cross-hatched target signals from the Radonimage. For FWS wavefield separation, rectangular extractions can causeincomplete or mixed extraction on the target wave component (as will beillustrated in FIG. 3B), thus degrading separation quality. ASE can useslant windows for FWS wavefield separation so that cross-hatched targetsignals can be efficiently captured according to given FWSconfigurations.

For extracting a target wave centered at (τ_(c), p_(c)) in the Radonparameter domain, a half window length n, can be determined. Forexample, n, can be determined so that the slanted window can enclose thetarget wave in the Radon domain. In some implementations, the velocityand time analysis performed at block 102 can be used to determine theslant window, The upper and lower boundary of the slanted window in ther direction can be determined by

$\begin{matrix}\left\{ {\begin{matrix}\begin{matrix}{{Upper}\text{:}\mspace{14mu} {\min\left( \left\lbrack {{\left( {\tau_{c} - n_{\tau}} \right) - {{\min \left( {{abs}\left( x_{a} \right)} \right)}*p_{a}}},} \right. \right.}} \\\left. \left. {\left( {\tau_{c} - n_{\tau}} \right) - {{\max \left( {{abs}\left( x_{a} \right)} \right)}*p_{a}}} \right\rbrack \right)\end{matrix} \\\begin{matrix}{{Lower}\text{:}\mspace{14mu} {\max\left( \left\lbrack {{\left( {\tau_{c} + n_{\tau}} \right) - {{\min \left( {{abs}\left( x_{a} \right)} \right)}*p_{a}}},} \right. \right.}} \\\left. \left. {\left( {\tau_{c} + n_{\tau}} \right) - {{\max \left( {{abs}\left( x_{a} \right)} \right)}*p_{a}}} \right\rbrack \right)\end{matrix}\end{matrix},} \right. & (5)\end{matrix}$

where abs denotes an operation of taking absolute value, min and maxdenote a minimum and a maximum operation, respectively, operator*inmin(abs(x_(a)))*p_(a) and max(abs(x_(a)))*p_(a) denotes anelement-by-element multiplication, and (τ_(c), p_(c)) is determined inblock 102. The resultant upper and lower boundary from Equation (5) arevectors having the same size as the vector p_(a), and elements in theupper and lower boundary vectors represent boundary values at thecorresponding slowness sampling values p_(a). In Equation (5), theanti-aliasing offset variable x_(a) enables an adaptive description ofthe cross-hatched target signals, and the minimum and maximum operationscan enlarge the window area for extraction. In some implementations, theextraction window can be further refined if the velocity range or thetime range of the target wave is available. For example, based on thevelocity range of the target wave determined in block 102, instead ofusing full vectors p_(a) and x_(a) in Equation (5), a subset of p_(a)and x_(a) corresponding to the determined velocity range can be used toderive the slant window. In some implementations, the half window lengthn_(τ) can be determined based on the time range of the target wavedetermined in block 102.

At block 108, signals of the target wave component can be recovered byperforming an inverse Radon transform on the extracted component in theRadon parameter domain. For example, an inverse Radon transform can beperformed on the actual offset spread x using Equation (3). In someimplementations, results of the inverse Radon transform can be complexnumbers, and the wave component can be recovered using the real part ofthe complex numbers and removing the imagery part.

FIGS. 2A-2C illustrate FWS wavefield separation for a compressionalwave, according to some implementations. FIG. 2A illustrates a syntheticFWS gather 200 a that simulates a compressional wave with a wave speedof 5000 m/s, recorded on 8 receivers with a spacing of 0.3048 m. Thesampling time interval for data recording is 1e-5 s, and the dominantfrequency of the synthetic FWS gather is 10 kHz. The horizontal andvertical axis in FIG. 2A represent an offset index and a time,respectively. FIG. 2B illustrates a Radon image 200 b after a directapplication of a traditional Radon transform on the synthetic FWS gather200 a. FIG. 2B shows that a traditional Radon transform can lead to ascattered energy map, indicating that the wave component isindistinguishable. FIG. 2C illustrates a Radon image 200 c afterapplying AALRT on the synthetic FWS gather 200 a. The dot 202 representsthe compressional wave component, which has a slowness 2e-3 s/m, thatis, an inverse of the wave speed 5000 m/s. FIG. 2C also shows that afterapplying AALRT that includes adopting the anti-aliasing offset x_(a) andperforming local interpolation, the Radon image 200 c becomes morefocused in the slowness(p)−intercept(τ) domain when compared to theRadon image 200 b. For example, traditional Radon transform generatesthe Radon image 200 b of slowness sampling values ranging from −2e-5 s/mto 2e-5 s/m, while AALRT generates the Radon image 200 c ofanti-aliasing slowness sampling values ranging from −1e-3 s/m to 1e-3s/m. In other words, wave components of a speed larger than 1000 m/s canbe effectively modelled and characterized in Radon parameter domainusing AALRT.

FIGS. 3A-4C illustrate FWS wavefield separation for a synthetic FWSgather, according to some implementations. The synthetic gathersimulates compressional, shear, and Stoneley waves with differentdominant frequencies, recorded by 8 receivers with a spacing of 0.3048m. FIGS. 3A-3C illustrate the wavefield separation in a Radon domain,and FIGS. 4A-4C illustrate wavefield in an offset(x)−time(t) domaincorresponding to FIGS. 3A-3C. FIGS. 4A-4C can be generated by performingan inverse Radon transform of FIGS. 3A-3C, respectively. FIG. 3A shows atotal wavefield 300 a including a compressional wave 302 and a shearwave 304 in a Radon domain, where linear moveout correction of thecompressional wave has been performed and the compressional wave 302 isthe target wave to be separated. FIG. 4A illustrates a total wavefield400 a of the synthetic FWS gather in an offset-time domain including acompressional wave 402 and a shear wave 404. FIG. 3B shows that a smallrectangular window is incapable of capturing complete features of thetarget wave component 302, while a large window 306 can easily cut intoother wave component such as the shear wave 304, leading to an impureseparation result as shown in FIG. 4B, where the shear wave 404 ispresent in addition to the target compressional wave 402. FIG. 3Cillustrates that a slant window 308 of ASE can extract the targetcomponent 302 without cutting into the shear wave 304. FIG. 4Cillustrates that ASE generates a clean separation result including thetarget compressional wave 402, but not the shear wave 404.

FIGS. 5A-5D illustrate FWS wavefield separation for a real FWS gather,according to some implementations. FIG. 5A illustrates the real FWS shotgather 500 a, where the vertical and horizontal axis represent a timesample index and an offset index of the FWS gather. The FWS gather 500 ashows that except for the shallow compressional wave component 508,shear and Stoneley waves are indistinguishable due to aliasing effects.Noise is also an unfavorable factor for wavefield separation, especiallyfor a deep Stoneley wave. FIGS. 5B-5D illustrate separation results byusing the described approach on the FWS gather 500 a. FIGS. 5B-5D showseparated compressional wave 502, Shear wave 504, and Stoneley wave 506,respectively, illustrating that the described approach can generatehigh-quality separation results in spite of practical adversaries suchas aliasing and noises.

FIG. 6 is a block diagram of an example computer system 600 used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the instant disclosure, according to some implementations.The illustrated computer 602 is intended to encompass any computingdevice such as a server, desktop computer, laptop/notebook computer,wireless data port, smart phone, personal data assistant (PDA), tabletcomputing device, one or more processors within these devices, or anyother suitable processing device, including physical or virtualinstances (or both) of the computing device. Additionally, the computer602 may comprise a computer that includes an input device, such as akeypad, keyboard, touch screen, or other device that can accept userinformation, and an output device that conveys information associatedwith the operation of the computer 602, including digital data, visual,or audio information (or a combination of information), or a graphicaluser interface (GUI).

The computer 602 can serve in a role as a client, network component, aserver, a database or other persistency, or any other component (or acombination of roles) of a computer system for performing the subjectmatter described in the instant disclosure. The illustrated computer 602is communicably coupled with a network 630. In some implementations, oneor more components of the computer 602 may be configured to operatewithin environments, including cloud-computing-based, local, global, orother environment (or a combination of environments).

At a high level, the computer 602 is an electronic computing deviceoperable to receive, transmit, process, store, or manage data andinformation associated with the described subject matter. According tosome implementations, the computer 602 may also include or becommunicably coupled with an application server, e-mail server, webserver, caching server, streaming data server, or other server (or acombination of servers).

The computer 602 can receive requests over network 630 from a clientapplication (for example, executing on another computer 602) andresponding to the received requests by processing the received requestsusing an appropriate software application(s). In addition, requests mayalso be sent to the computer 602 from internal users (for example, froma command console or by other appropriate access method), external orthird-parties, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

Each of the components of the computer 602 can communicate using asystem bus 603. In some implementations, any or all of the components ofthe computer 602, both hardware or software (or a combination ofhardware and software), may interface with each other or the interface604 (or a combination of both) over the system bus 603 using anapplication programming interface (API) 612 or a service layer 613 (or acombination of the API 612 and service layer 613). The API 612 mayinclude specifications for routines, data structures, and objectclasses. The API 612 may be either computer-language independent ordependent and refer to a complete interface, a single function, or evena set of APIs. The service layer 613 provides software services to thecomputer 602 or other components (whether or not illustrated) that arecommunicably coupled to the computer 602. The functionality of thecomputer 602 may be accessible for all service consumers using thisservice layer. Software services, such as those provided by the servicelayer 613, provide reusable, defined functionalities through a definedinterface. For example, the interface may be software written in JAVA,C++, or other suitable language providing data in extensible markuplanguage (XML) format or other suitable format. While illustrated as anintegrated component of the computer 602, alternative implementationsmay illustrate the API 612 or the service layer 613 as stand-alonecomponents in relation to other components of the computer 602 or othercomponents (whether or not illustrated) that are communicably coupled tothe computer 602. Moreover, any or all parts of the API 612 or theservice layer 613 may be implemented as child or sub-modules of anothersoftware module, enterprise application, or hardware module withoutdeparting from the scope of this disclosure.

The computer 602 includes an interface 604. Although illustrated as asingle interface 604 in FIG. 6, two or more interfaces 604 may be usedaccording to particular needs, desires, or particular implementations ofthe computer 602. The interface 604 is used by the computer 602 forcommunicating with other systems that are connected to the network 630(whether illustrated or not) in a distributed environment. Generally,the interface 604 comprises logic encoded in software or hardware (or acombination of software and hardware) and is operable to communicatewith the network 630. More specifically, the interface 604 may comprisesoftware supporting one or more communication protocols associated withcommunications such that the network 630 or interface's hardware isoperable to communicate physical signals within and outside of theillustrated computer 602.

The computer 602 includes a processor 605. Although illustrated as asingle processor 605 in FIG. 6, two or more processors may be usedaccording to particular needs, desires, or particular implementations ofthe computer 602. Generally, the processor 605 executes instructions andmanipulates data to perform the operations of the computer 602 and anyalgorithms, methods, functions, processes, flows, and procedures asdescribed in the instant disclosure.

The computer 602 also includes a database 606 that can hold data for thecomputer 602 or other components (or a combination of both) that can beconnected to the network 630 (whether illustrated or not). For example,database 606 can be an in-memory, conventional, or other type ofdatabase storing data consistent with this disclosure. In someimplementations, database 606 can be a combination of two or moredifferent database types (for example, a hybrid in-memory andconventional database) according to particular needs, desires, orparticular implementations of the computer 602 and the describedfunctionality. Although illustrated as a single database 606 in FIG. 6,two or more databases (of the same or combination of types) can be usedaccording to particular needs, desires, or particular implementations ofthe computer 602 and the described functionality. While database 606 isillustrated as an integral component of the computer 602, in alternativeimplementations, database 606 can be external to the computer 602. Forexample, the database 606 can hold FWS data.

The computer 602 also includes a memory 607 that can hold data for thecomputer 602 or other components (or a combination of both) that can beconnected to the network 630 (whether illustrated or not). For example,memory 607 can be random access memory (RAM), read-only memory (ROM),optical, magnetic, and the like storing data consistent with thisdisclosure. In some implementations, memory 607 can be a combination oftwo or more different types of memory (for example, a combination of RAMand magnetic storage) according to particular needs, desires, orparticular implementations of the computer 602 and the describedfunctionality. Although illustrated as a single memory 607 in FIG. 6,two or more memories 607 (of the same or combination of types) can beused according to particular needs, desires, or particularimplementations of the computer 602 and the described functionality.While memory 607 is illustrated as an integral component of the computer602, in alternative implementations, memory 607 can be external to thecomputer 602.

The application 608 is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 602, particularly with respect tofunctionality described in this disclosure. For example, application 608can serve as one or more components, modules, or applications. Further,although illustrated as a single application 608, the application 608may be implemented as multiple applications 608 on the computer 602. Inaddition, although illustrated as integral to the computer 602, inalternative implementations, the application 608 can be external to thecomputer 602.

There may be any number of computers 602 associated with, or externalto, a computer system containing computer 602, each computer 602communicating over network 630. Further, the term “client,” “user,” andother appropriate terminology may be used interchangeably as appropriatewithout departing from the scope of this disclosure. Moreover, thisdisclosure contemplates that many users may use one computer 602, orthat one user may use multiple computers 602.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Implementations of the subject matter described inthis specification can be implemented as one or more computer programs,that is, one or more modules of computer program instructions encoded ona tangible, non-transitory, computer-readable computer-storage mediumfor execution by, or to control the operation of, data processingapparatus. Alternatively, or additionally, the program instructions canbe encoded in/on an artificially generated propagated signal, forexample, a machine-generated electrical, optical, or electromagneticsignal that is generated to encode information for transmission tosuitable receiver apparatus for execution by a data processingapparatus. The computer-storage medium can be a machine-readable storagedevice, a machine-readable storage substrate, a random or serial accessmemory device, or a combination of computer-storage mediums.

The term “real-time,” “real time,” “realtime,” “real (fast) time (RFT),”“near(ly) real-time (NRT),” “quasi real-time,” or similar terms (asunderstood by one of ordinary skill in the art), means that an actionand a response are temporally proximate such that an individualperceives the action and the response occurring substantiallysimultaneously. For example, the time difference for a response todisplay (or for an initiation of a display) of data following theindividual's action to access the data may be less than 1 ms, less than1 sec., or less than 5 secs. While the requested data need not bedisplayed (or initiated for display) instantaneously, it is displayed(or initiated for display) without any intentional delay, taking intoaccount processing limitations of a described computing system and timerequired to, for example, gather, accurately measure, analyze, process,store, or transmit the data.

The terms “data processing apparatus,” “computer,” or “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware and encompass all kinds ofapparatus, devices, and machines for processing data, including by wayof example, a programmable processor, a computer, or multiple processorsor computers. The apparatus can also be or further include specialpurpose logic circuitry, for example, a central processing unit (CPU),an FPGA (field programmable gate array), or an ASIC(application-specific integrated circuit). In some implementations, thedata processing apparatus or special purpose logic circuitry (or acombination of the data processing apparatus or special purpose logiccircuitry) may be hardware- or software-based (or a combination of bothhardware- and software-based). The apparatus can optionally include codethat creates an execution environment for computer programs, forexample, code that constitutes processor firmware, a protocol stack, adatabase management system, an operating system, or a combination ofexecution environments. The present disclosure contemplates the use ofdata processing apparatuses with or without conventional operatingsystems, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, or anyother suitable conventional operating system.

A computer program, which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, for example,one or more scripts stored in a markup language document, in a singlefile dedicated to the program in question, or in multiple coordinatedfiles, for example, files that store one or more modules, sub-programs,or portions of code. A computer program can be deployed to be executedon one computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork. While portions of the programs illustrated in the variousfigures are shown as individual modules that implement the variousfeatures and functionality through various objects, methods, or otherprocesses, the programs may instead include a number of sub-modules,third-party services, components, libraries, and such, as appropriate.Conversely, the features and functionality of various components can becombined into single components as appropriate. Thresholds used to makecomputational determinations can be statically, dynamically, or bothstatically and dynamically determined.

The methods, processes, or logic flows described in this specificationcan be performed by one or more programmable computers executing one ormore computer programs to perform functions by operating on input dataand generating output. The methods, processes, or logic flows can alsobe performed by, and apparatus can also be implemented as, specialpurpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors, both, or any other kindof CPU. Generally, a CPU will receive instructions and data from aread-only memory (ROM) or a random access memory (RAM), or both. Theessential elements of a computer are a CPU, for performing or executinginstructions, and one or more memory devices for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to, receive data from or transfer data to, or both, one or moremass storage devices for storing data, for example, magnetic,magneto-optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, for example, a mobile telephone, a personal digital assistant(PDA), a mobile audio or video player, a game console, a globalpositioning system (GPS) receiver, or a portable storage device, forexample, a universal serial bus (USB) flash drive, to name just a few.

Computer-readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data include allforms of non-volatile memory, media and memory devices, including by wayof example semiconductor memory devices, for example, erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices;magnetic disks, for example, internal hard disks or removable disks;magneto-optical disks; and CD-ROM, DVD+/−R, DVD-RAM, and DVD-ROM disks.The memory may store various objects or data, including caches, classes,frameworks, applications, backup data, jobs, web pages, web pagetemplates, database tables, repositories storing dynamic information,and any other appropriate information including any parameters,variables, algorithms, instructions, rules, constraints, or referencesthereto. Additionally, the memory may include any other appropriatedata, such as logs, policies, security or access data, reporting files,as well as others. The processor and the memory can be supplemented by,or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, for example, a CRT (cathode ray tube), LCD(liquid crystal display), LED (Light Emitting Diode), or plasma monitor,for displaying information to the user and a keyboard and a pointingdevice, for example, a mouse, trackball, or trackpad by which the usercan provide input to the computer. Input may also be provided to thecomputer using a touchscreen, such as a tablet computer surface withpressure sensitivity, a multi-touch screen using capacitive or electricsensing, or other type of touchscreen. Other kinds of devices can beused to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, forexample, visual feedback, auditory feedback, or tactile feedback; andinput from the user can be received in any form, including acoustic,speech, or tactile input. In addition, a computer can interact with auser by sending documents to and receiving documents from a device thatis used by the user; for example, by sending web pages to a web browseron a user's client device in response to requests received from the webbrowser.

The term “graphical user interface,” or “GUI,” may be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI may represent any graphical user interface, includingbut not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI may include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements may be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server, or that includes afront-end component, for example, a client computer having a graphicaluser interface or a Web browser through which a user can interact withan implementation of the subject matter described in this specification,or any combination of one or more such back-end, middleware, orfront-end components. The components of the system can be interconnectedby any form or medium of wireline or wireless digital data communication(or a combination of data communication), for example, a communicationnetwork. Examples of communication networks include a local area network(LAN), a radio access network (RAN), a metropolitan area network (MAN),a wide area network (WAN), Worldwide Interoperability for MicrowaveAccess (WIMAX), a wireless local area network (WLAN) using, for example,802.11 a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 orother protocols consistent with this disclosure), all or a portion ofthe Internet, or any other communication system or systems at one ormore locations (or a combination of communication networks). The networkmay communicate with, for example, Internet Protocol (IP) packets, FrameRelay frames, Asynchronous Transfer Mode (ATM) cells, voice, video,data, or other suitable information (or a combination of communicationtypes) between network addresses.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particularimplementations of particular inventions. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented, in combination, in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations, separately, or in any suitable sub-combination.Moreover, although previously-described features may be described asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can, in some cases, beexcised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations may be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously-described implementations should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously-described example implementations do notdefine or constrain this disclosure. Other changes, substitutions, andalterations are also possible without departing from the spirit andscope of this disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

1. A method, comprising: receiving full-waveform sonic (FWS) data;performing an anti-aliasing linear Radon transform on the received FWSdata; extracting Radon-transformed FWS data corresponding to a wavecomponent using a slanted window; and determining signals of the wavecomponent by performing an inverse Radon transform on the extractedRadon-transformed FWS data.
 2. The method of claim 1, wherein the wavecomponent is one of a compressional wave, a shear wave, or a Stoneleywave.
 3. The method of claim 1, wherein performing the anti-aliasinglinear Radon transform includes: determining a set of anti-aliasingoffset values and a set of slowness values based on an anti-aliasingoffset spacing; generating interpolated FWS data based on the receivedFWS data and the set of anti-aliasing offset values; and performing aRadon transform on the interpolated FWS data based on the set ofanti-aliasing offset values and the set of slowness values.
 4. Themethod of claim 3, wherein the anti-aliasing offset spacingΔx_(a)=0.5*Δt V_(min), where Δt is a sampling time interval of thereceived FWS data, and V_(min) is a lower boundary of a velocity of thewave component.
 5. The method of claim 1, further comprising performinga velocity analysis to determine a central velocity, a velocity range, acentral time, and a time range of the wave component.
 6. The method ofclaim 5, wherein the slanted window is centered at a point determinedbased on the central velocity and the central time of the wavecomponent, and the slanted window has a size determined based on thevelocity range and the time range of the wave component.
 7. The methodof claim 1, wherein performing an inverse Radon transform on theextracted Radon-transformed FWS data comprises performing the inverseRadon transform based on a set of actual offset values.
 8. The method ofclaim 1, wherein the received FWS data is a common shot gather.
 9. Asystem, comprising: a computer memory; and one or more hardwareprocessor interoperably coupled with the computer memory and configuredto perform operations comprising: receiving full-waveform sonic (FWS)data; performing an anti-aliasing linear Radon transform on the receivedFWS data; extracting Radon-transformed FWS data corresponding to a wavecomponent using a slanted window; and determining signals of the wavecomponent by performing an inverse Radon transform on the extractedRadon-transformed FWS data.
 10. The system of claim 9, wherein the wavecomponent is one of a compressional wave, a shear wave, or a Stoneleywave.
 11. The system of claim 9, wherein performing the anti-aliasinglinear Radon transform includes: determining a set of anti-aliasingoffset values and a set of slowness values based on an anti-aliasingoffset spacing; generating interpolated FWS data based on the receivedFWS data and the set of anti-aliasing offset values; and performing aRadon transform on the interpolated FWS data based on the set ofanti-aliasing offset values and the set of slowness values.
 12. Thesystem of claim 11, wherein the anti-aliasing offset spacingΔx_(a)=0.5*Δt*V_(min), where Δt is a sampling time interval of thereceived FWS data, and V_(min) is a lower boundary of a velocity of thewave component.
 13. The system of claim 9, wherein the operationsfurther comprise performing a velocity analysis to determine a centralvelocity, a velocity range, a central time, and a time range of the wavecomponent.
 14. The system of claim 13, wherein the slanted window iscentered at a point determined based on the central velocity and thecentral time of the wave component, and the slanted window has a sizedetermined based on the velocity range and the time range of the wavecomponent.
 15. A non-transitory, computer-readable medium storing one ormore instructions executable by a computer system to perform operationscomprising: receiving full-waveform sonic (FWS) data; performing ananti-aliasing linear Radon transform on the received FWS data;extracting Radon-transformed FWS data corresponding to a wave componentusing a slanted window; and determining signals of the wave component byperforming an inverse Radon transform on the extracted Radon-transformedFWS data.
 16. The non-transitory, computer-readable medium of claim 15,wherein the wave component is one of a compressional wave, a shear wave,or a Stoneley wave.
 17. The non-transitory, computer-readable medium ofclaim 15, wherein performing the anti-aliasing linear Radon transformincludes: determining a set of anti-aliasing offset values and a set ofslowness values based on an anti-aliasing offset spacing; generatinginterpolated FWS data based on the received FWS data and the set ofanti-aliasing offset values; and performing a Radon transform on theinterpolated FWS data based on the set of anti-aliasing offset valuesand the set of slowness values.
 18. The non-transitory,computer-readable medium of claim 17, wherein the anti-aliasing offsetspacing Δx_(a)=0.5*Δt*V_(min), where Δt is a sampling time interval ofthe received FWS data, and V_(min) is a lower boundary of a velocity ofthe wave component.
 19. The non-transitory, computer-readable medium ofclaim 15, wherein the operations further comprise performing a velocityanalysis to determine a central velocity, a velocity range, a centraltime, and a time range of the wave component.
 20. The non-transitory,computer-readable medium of claim 19, wherein the slanted window iscentered at a point determined based on the central velocity and thecentral time of the wave component, and the slanted window has a sizedetermined based on the velocity range and the time range of the wavecomponent.